Section: Overall Objectives

Overall Objectives

One of the key challenges in the study of biological systems is understanding how the static information recorded in the genome is interpreted to become dynamic systems of cooperating and competing biomolecules. MAGNOME addresses this challenge through the development of informatic techniques for multi-scale modeling and large-scale comparative genomics:

  • logical and object models for knowledge representation

  • stochastic hierarchical models for behavior of complex systems, formal methods

  • algorithms for sequence analysis, and

  • data mining and classification.

We use genome-scale comparisons of eukaryotic organisms to build modular and hierarchical hybrid models of cell behavior that are studied using multi-scale stochastic simulation and formal methods. Our research program builds on our experience in comparative genomics, modeling of protein interaction networks, and formal methods for multi-scale modeling of complex systems.

New high-throughput technologies for DNA sequencing have radically reduced the cost of acquiring genome and transcriptome data, and introduced new strategies for whole genome sequencing. The result has been an increase in data volumes of several orders of magnitude, as well has a greatly increased density of genome sequences within phylogenetically constrained groups of species. Magnome develops efficient techniques for dealing with these increased data volumes, and the combinatorial challenges of dense multi-genome comparison.